Effects of High-Velocity Spinal Manipulation on Quality of Life, Pain and Spinal Curvature in Children with Idiopathic Scoliosis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Scoliosis is a condition that involves deformation of the spine in the coronal plane and commonly appears in childhood or adolescence, significantly limiting a person's life. The cause is multifactorial, and treatment aims to improve the spinal curvature, prevent major pathologies, and enhance aesthetics. The objective of this review was to determine whether high-velocity low-amplitude (HVLA) spinal manipulation is more effective than other treatments for children with idiopathic scoliosis (IS). METHODS: The PubMed, Web of Science, Scopus and PEDro databases were searched for both clinical trials and cohort studies. Methodological quality was assessed via the PEDro scale (for clinical trials) and the Newcastle-Ottawa scale (for observational studies). The protocol of this systematic review was registered in PROSPERO (CRD42024532442). RESULTS: Five studies were selected for review. The results indicated moderate improvements in pain and the Cobb angle and limited improvements in quality of life. CONCLUSIONS: HVLA spinal manipulation does not seem to have significant effects on reducing spinal deformity in IS patients, nor does it significantly impact quality of life. However, this therapy may have significant effects on reducing pain in these patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".